Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study

Yaqi Zhang; Yongxia Han; Peng Gao; Yifu Mo; Shiying Hao; Jia Huang; Fangfan Ye; Zhen Li; Le Zheng; Xiaoming Yao; Zhen Li; Xiaodong Li; Xiaofang Wang; Chao-Jung Huang; Bo Jin; Yani Zhang; Gabriel Yang; Shaun T Alfreds; Laura Kanov; Karl G Sylvester; Eric Widen; Licheng Li; Xuefeng Ling

doi:10.2196/23606

Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study

JMIR Med Inform. 2021 Feb 17;9(2):e23606. doi: 10.2196/23606.

Authors

Yaqi Zhang^#^{1

2}, Yongxia Han^#^{1

2}, Peng Gao^#^{2

3}, Yifu Mo^#^{1

4}, Shiying Hao^{5

6}, Jia Huang^{2

7}, Fangfan Ye^{2

8}, Zhen Li², Le Zheng^{5

6}, Xiaoming Yao⁹, Zhen Li^{2

10}, Xiaodong Li^{2

11}, Xiaofang Wang¹², Chao-Jung Huang¹³, Bo Jin¹⁴, Yani Zhang¹⁵, Gabriel Yang¹⁶, Shaun T Alfreds¹⁷, Laura Kanov¹⁴, Karl G Sylvester², Eric Widen¹⁴, Licheng Li¹, Xuefeng Ling^{2

6}

Affiliations

¹ School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.
² Department of Surgery, Stanford University, Stanford, CA, United States.
³ College of Pharmacy, Shandong University of Traditional Chinese Medicine, Shandong, China.
⁴ China Southern Power Grid Company Limited, Guangzhou, China.
⁵ Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.
⁶ Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States.
⁷ Department of Critical Care Medicine, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
⁸ Department of Anesthesiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
⁹ Translational Medicine Laboratory, Queen Mary Hospital, Hong Kong University, Hong Kong, China.
¹⁰ School of Electrical Engineering, Southeast University, Nanjing, China.
¹¹ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.
¹² Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China.
¹³ National Taiwan University-Stanford Joint Program Office of Artificial Intelligence in Biotechnology, Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare, Taipei, China.
¹⁴ HBI Solutions Inc, Palo Alto, CA, United States.
¹⁵ Tianjin Yunjian Medical Laboratory Institute Co Ltd, Tianjing, China.
¹⁶ The Harker School, San Jose, CA, United States.
¹⁷ HealthInfoNet, Portland, ME, United States.

^# Contributed equally.

PMID: 33595452
PMCID: PMC7929752
DOI: 10.2196/23606

Abstract

Background: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death.

Objective: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia.

Methods: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

Results: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year.

Conclusions: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

Keywords: cardiac dysrhythmia; electronic health records; prospective case finding; risk stratification.

©Yaqi Zhang, Yongxia Han, Peng Gao, Yifu Mo, Shiying Hao, Jia Huang, Fangfan Ye, Zhen Li, Le Zheng, Xiaoming Yao, Zhen Li, Xiaodong Li, Xiaofang Wang, Chao-Jung Huang, Bo Jin, Yani Zhang, Gabriel Yang, Shaun T Alfreds, Laura Kanov, Karl G Sylvester, Eric Widen, Licheng Li, Xuefeng Ling. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.02.2021.